A6000 vs H200
Explore a head to head comparison of specifications, performance, and pricing.
A6000
The NVIDIA A6000 delivers high-performance computing capabilities for AI, machine learning, and data science applications.
H200
The NVIDIA H200 is an advanced Hopper-based GPU that significantly boosts performance for generative AI, LLM, and HPC workloads with enhanced memory and bandwidth.
A6000 vs H200: Which Should You Choose?
The H200 offers 141 GB of VRAM — 3× the 48 GB on the A6000 — making it better suited for large model workloads that require holding more parameters in GPU memory. On FP16 throughput, the H200 delivers 267.6 TFLOPS versus 38.71 TFLOPS on the A6000 — 7× faster for mixed-precision training and inference. Memory bandwidth favors the A6000 at 0.77 TB/s compared to 0.00 TB/s on the H200, which directly impacts inference latency for memory-bandwidth-bound models. Architecturally, the A6000 is built on Ampere while the H200 uses Hopper, reflecting different generational capabilities and optimizations. On Shadeform, the A6000 starts from $0.49/hr versus $2.45/hr for the H200 — 400% more expensive — reflecting the performance premium. The H200 is available across 7 cloud providers on Shadeform compared to 6 for the A6000, giving more options for region and pricing flexibility.
A6000 — Best Use Cases
- •General-purpose deep learning training
- •Fine-tuning models up to 13B parameters
- •AI inference at moderate throughput
- •Computer vision and NLP workloads
Choose A6000 when:
- ✓48 GB VRAM is sufficient for your workload
- ✓Cost efficiency is your primary concern
- ✓Your workload does not require peak FP16 throughput
- ✓Your preferred provider already has availability
H200 — Best Use Cases
- •Training large language models (7B–405B parameters)
- •High-throughput LLM inference
- •Mixture-of-experts and transformer workloads
- •Distributed multi-GPU training runs
Choose H200 when:
- ✓You need 141 GB+ VRAM for large models or long context windows
- ✓Maximum performance justifies the higher cost
- ✓You are training large models or running high-throughput inference
- ✓You need flexibility across multiple cloud providers or regions
See how the A6000 & H200 compare
Compare detailed hardware specifications and average pricing for the A6000 and H200.
Compare Hardware Specifications
| A6000 | H200 | |
|---|---|---|
| GPU Type | A6000 | H200 |
| VRAM per GPU | 48 GB | 141 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Ampere | Hopper |
| Interconnect | PCIe Gen4 | SXM5 |
| Memory Bandwidth | 768 GB/s | 4.8 TB/s |
| FP16 TFLOPS | 38.71 TFLOPS (1:1) | 267.6 TFLOPS (4:1) |
| CUDA Cores | 10752 | 16896 |
| Tensor Cores | 336 (3rd Gen) | 528 (4th Gen) |
| RT Cores | 84 (2nd Gen) | N/A |
| Base Clock | 1410 MHz | 1500 MHz |
| Boost Clock | 1800 MHz | 1980 MHz |
| TDP | 300W | 350-700W |
| Process Node | TSMC 8nm | TSMC 4N |
| Data Formats | INT8, BF16, FP16, TF32, FP32 | FP8, INT8, BF16, FP16, TF32, FP32, FP64 |
Compare Average On-Demand Pricing
| A6000 | H200 | |
|---|---|---|
| 1 GPU | $0.94 /hr | $3.33 /hr |
| 2 GPUs | $1.89 /hr | $14.79 /hr |
| 4 GPUs | $3.77 /hr | $7.60 /hr |
| 8 GPUs | $4.16 /hr | $23.48 /hr |
Frequently Asked Questions: A6000 vs H200
The main differences are VRAM (48 GB vs 141 GB), FP16 throughput (38.71 vs 267.6 TFLOPS), architecture (Ampere vs Hopper). The A6000 uses the Ampere architecture while the H200 is based on Hopper, giving each GPU different generational capabilities.
The H200 is generally better for large language model training due to its higher throughput and 141 GB of VRAM, which allows fitting larger models or larger batch sizes in a single pass. For smaller models or fine-tuning tasks where cost matters more, both GPUs can be effective.
On Shadeform, the A6000 is available from $0.49/hr. The H200 starts from $2.45/hr. Prices vary by provider, region, and contract length. Reserved commitments can reduce hourly costs significantly compared to on-demand pricing.
The H200 has more VRAM at 141 GB, compared to 48 GB on the A6000. Higher VRAM allows you to run larger models without quantization, use longer context windows, and process larger batch sizes — all of which improve throughput and reduce latency for memory-bound workloads.
Based on TFLOPS per dollar, the H200 offers better raw compute value at current Shadeform on-demand rates. However, the best choice depends on your specific workload — if you need the extra VRAM or throughput of the A6000, paying the premium may be justified by faster job completion and lower total cost.
The H200 is currently available across 7 cloud providers on Shadeform's network, compared to 6 for the A6000. Shadeform lets you deploy either GPU across all available providers from a single platform, so you can always find available capacity without manually checking each cloud.
Mixing different GPU types in a single training cluster is generally not recommended, as it creates performance bottlenecks where faster GPUs wait for slower ones. For best results, use a homogeneous cluster of either A6000 or H200. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
Explore A6000 & H200 Instances
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